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developing-llamaindex-systems

1 forks
99
A

Production-grade agentic system development with LlamaIndex in Python. Covers semantic ingestion (SemanticSplitterNodeParser, CodeSplitter, IngestionPipeline), retrieval strategies (BM25Retriever, hybrid search, alpha weighting), PropertyGraphIndex with graph stores (Neo4j), context RAG (RouterQueryEngine, SubQuestionQueryEngine, LLMRerank), agentic orchestration (ReAct, Workflows, FunctionTool), and observability (Arize Phoenix). Use when asked to "build a LlamaIndex agent", "set up semantic...

#references#semantic chunking#semantic#build#graph#Build Index#pattern#Workflow pattern
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Installation for Agentic Skill

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skilz install SpillwaveSolutions/developing-llamaindex-systems/developing-llamaindex-systems
skilz install SpillwaveSolutions/developing-llamaindex-systems/developing-llamaindex-systems --agent opencode
skilz install SpillwaveSolutions/developing-llamaindex-systems/developing-llamaindex-systems --agent codex
skilz install SpillwaveSolutions/developing-llamaindex-systems/developing-llamaindex-systems --agent gemini

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Works with 14 AI coding assistants

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Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/SpillwaveSolutions/developing-llamaindex-systems
2. Copy the agent skill directory:
cp -r developing-llamaindex-systems ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

Claude Desktop Guide Claude Code Guide Troubleshooting

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Agentic Skill Details

Owner
SpillwaveSolutions (GitHub)
Repository
developing-llamaindex-systems
Forks
1
Type
Technical
Meta-Domain
development
Primary Domain
python
Market Score
99
Agentic Skill Grades →

Agent Skill Grade

A
Score: 99/100 Click to see breakdown

Score Breakdown

Spec Compliance
15/15
PDA Architecture
28/30
Ease of Use
24/25
Writing Style
9/10
Utility
19/20
Modifiers: +4

Areas to Improve

  • Missing TOC in SKILL.md
  • Verbose complete examples
  • Limited input/output examples

Recommendations

  • Add trigger phrases to description for discoverability
  • Add table of contents for files over 100 lines

Graded: 2026-01-19

Developer Feedback

I took a look at your developing-llamaindex-systems skill and wanted to share some thoughts.

Links:

The TL;DR

You're at 99/100, solid A territory. This is based on Anthropic's best practices for Claude Code skills. Your strongest area is Spec Compliance (15/15) — frontmatter is clean, triggers are specific, and the skill metadata is dialed in. The weakest spot is Writing Style (9/10), mostly around code example length rather than clarity.

What's Working Well

  • Trigger coverage is excellent — You've got 15+ activation phrases like "build a LlamaIndex agent," "implement hybrid search," and specific component names (PropertyGraphIndex, SemanticSplitterNodeParser). This means developers will actually find this skill when they need it.
  • Progressive Disclosure architecture is solid — 509-line SKILL.md as hub with 6 focused reference files (400-600 lines each) plus executable scripts. That's the right structure for token efficiency.
  • Decision trees + Troubleshooting pattern works — The Diagnose→Fix→Verify flow is practical. You're not just explaining concepts; you're giving people a path to solve problems.
  • Real problem coverage — Semantic chunking, knowledge graphs, query routing, observability. You're hitting gaps that actually exist in how people use LlamaIndex.

The Big One: Add a Table of Contents to SKILL.md

Why it matters: SKILL.md is 509 lines but jumps straight into content. Developers skimming your skill can't quickly navigate to what they need. This is friction in a document that long.

The fix: Add a ## Contents section right after the title listing all major sections: Quick Start, Architecture Overview, Decision Trees, Common Patter...

AI-Detected Topics

Extracted using NLP analysis

references semantic chunking semantic build graph Build Index pattern Workflow pattern retrieval orchestration.md references

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